Estimating the cost of achieving basic water, sanitation, hygiene, and waste management services in public health-care facilities in the 46 UN designated least-developed countries: a modelling study

Summary Background An alarming number of public health-care facilities in low-income and middle-income countries lack basic water, sanitation, hygiene (WASH), and waste management services. This study estimates the costs of achieving full coverage of basic WASH and waste services in existing public health facilities in the 46 UN designated least-developed countries (LDCs). Methods In this modelling study, in-need facilities were quantified by combining published counts of public facilities with estimated basic WASH and waste service coverage. Country-specific per-facility capital and recurrent costs to deliver basic services were collected via survey of country WASH experts and officials between Sept 24 and Dec 24, 2020. Baseline cost estimates were modelled and discounted by 5% per year. Key assumptions were adjusted to produce lower and upper estimates, including adjusting the discount rate to 8% and 3% per year, respectively. Findings An estimated US$6·5 billion to $9·6 billion from 2021 to 2030 is needed to achieve full coverage of basic WASH and waste services in public health facilities in LDCs. Capital costs are $2·9 billion to $4·8 billion and recurrent costs are $3·6 billion to $4·8 billion over this time period. A mean of $0·24–0·40 per capita in capital investment is needed each year, and annual operations and maintenance costs are expected to increase from $0·10 in 2021 to $0·39–0·60 in 2030. Waste management accounts for the greatest share of costs, requiring $3·7 billion (46·6% of the total) in the baseline estimates, followed by $1·8 billion (23·1%) for sanitation, $1·5 billion (19·5%) for water, and $845 million (10·7%) for hygiene. Needs are greatest for non-hospital facilities ($7·4 billion [94%] of $7·9 billion) and for facilities in rural areas ($5·3 billion [68%]). Interpretation Investment will need to increase to reach full coverage of basic WASH and waste services in public health facilities. Financial needs are modest compared with current overall health and WASH spending, and better service coverage will yield substantial health benefits. To sustain services and prevent degradation and early replacement, countries will need to routinely budget for operations and maintenance of WASH and waste management assets. Funding WHO (including underlying grants from the governments of Japan, the Netherlands, and the UK), World Bank (including an underlying grant from the Global Water Security and Sanitation Partnership), and UNICEF. Translations For the Arabic, French and Portuguese translations of the abstract see Supplementary Materials section.


Country survey
This sub-section describes the origins and nature of the cost data collected by UNICEF between September 24, 2020 and December 24, 2020. In many cases, data were provided on the condition that they would only be used to generate multi-country estimates in a global study-there is no official approval from relevant authorities to publish country-identified cost data. For this reason, no country-specific costs or resource needs estimates are presented in the main paper or in this appendix. Readers seeking additional information about these data should contact Jorge Alvarez-Sala Torreano (jalvarezsala@unicef.org).
A data collection instrument was circulated to the UNICEF Chief of WASH in 59 countries, including all 46 LDCs. Of these, information was received back from 44 countries, including 40 LDCs (table S2). It was originally hoped that the cost analysis would cover all surveyed countries, but due to data limitations (e.g., country-level estimates of WASH and waste service coverage), only the LDCs were ultimately included. Consequently, only data received from LDCs were used in this study. At the time of data collection, there were 47 LDCs; following Vanuatu's graduation from LDC classification in December 2020, it was removed from the cost model, leaving 46 countries. UNICEF is exploring ways to make use of cost data received from non-LDCs to extend the analysis presented here. * Vanuatu was classified as an LDC at the time of data collection and then graduated in December 2020. The preliminary resource needs estimate within the 2020 global progress report on WASH in health care facilities 1 included Vanuatu, whereas the country is excluded from the findings presented in this study.
The data collection instrument was designed to rapidly capture the main cost drivers for WASH and waste services in health care facilities (table S3), informed by consultations with experts at UNICEF and WHO, and from practical experience supporting the national planning and budgeting exercise for Ethiopia's One WASH National Programme (OWNP) Phase II. b Respondents were asked to submit per-facility investments required to achieve basic service levels in hospitals and non-hospitals, differentiating between one-time capital costs and annual recurrent costs. c All cost data used in this study were provided by survey respondents in 2020 United States dollars. * Other costs included one-time and annual costs for WASH-related training, planning, and monitoring activities. These were not incorporated into the analysis. † Costs for connections to networks only included connection costs from the facility to the network and within the facility, not any costs associated with construction, expansion, operation, or maintenance of the network itself.
Detailed instructions were transmitted to respondents with the data collection instrument. Respondents were explicitly directed to report average costs, mindful of the considerable variability likely to exist in their countries (see Box S1). Beyond the simplified facility types (hospitals, non-hospitals) and locations (urban, rural), no specific assumptions or guidance were provided regarding facility size. It was assumed the respondents accounted for variation in facility sizes when providing average costs.
The instructions recommended that respondents submit the average costs of new infrastructure rather than rehabilitating existing infrastructure unless most facilities in the country only needed rehabilitation. Information in respondent comments accompanying their submissions and subsequent communications with some respondents made it clear that it was too challenging to quickly estimate average costs reflecting different levels of investment needs within a country's facility stock. Instead, respondents consistently provided costs corresponding to new infrastructure, as if facilities had no WASH and waste services. Consequently, additional rules were developed to assign different costs to facilities with "limited" versus "no" services at baseline (see section 5).
Monitoring definitions from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) for basic service levels for WASH in health care facilities were also provided to respondents, excerpted directly from the original source 5 (figures S1-S5). It was not feasible to assess how rigorously respondents determined average costs. For example, if respondents based non-hospital costs primarily on larger health centres, this study may have overestimated costs associated with smaller clinics and health posts, which were b See https://www.unicef.org/ethiopia/reports/one-wash-national-programme. c The definition of capital and recurrent costs here aligns with the terms "capital expenditure (CapEx)" and "operations and maintenance (O&M)" commonly used in WASH costing studies.
nearly 60% of the non-hospital facilities included in the analysis. Factors mitigating the potential bias are noted in the main paper.
Box S1. Instructions accompanying the data collection instrument Important notes. Please read carefully before completing the form.
Thank you for being part of this important initiative that aims at making a business case for WASH investments in HCF. This form has been designed to collect key information that will be used to estimate national and global costs for achieving universal access to WASH in HCF. We are aware that some information might not be easily available and that there might be significant variables and variability across the country. What we aim is to collect the average costs based on the available data and your experience in the country.
When estimating the costs, you need to estimate them based on the amounts NEEDED to achieve JMP's basic service level, NOT the average CURRENT investment in HCF. This is very important for countries where the current levels of investments are insufficient to meet the JMP standards of basic service. You might however have some HCFs which meet the national and JMP standards of basic service, and which can be used as a benchmark for establishing the costs.
For CAPEX estimates please consider the costs of moving from JMP's "limited" or "no service" to" basic" service level. Those investments might be different if HCFs require a brand new infrastructure or a mayor rehabilitation of the existing infrastructures. Unless the majority of those limited/no-service HCFs have existing infrastructures that just require rehabilitation, we recommend that you estimate the cost of construction of a new infrastructure (i.e. construction of a new sanitary block rather than rehabilitating).
Assumptions: In the maintenance costs of infrastructures, you just need to consider the regular maintenance costs (i.e. replacement of components or spare parts), not the cost of mayor renovations/rehabilitations at the end of the lifespan of the whole infrastructure.
Inflation will be considered for the global calculations, but you don't need to provide any information related to inflation or factor it in your estimates; except if you are using old unit costs that you will need to adjust to actual costs in 2020.
Please use the "comments" section to include any additional information that you consider relevant or to clarify any assumptions that you made.
Feel free to reach us if you have any questions in relation to this form. Given that this study does not constitute human subjects research, no formal ethics approval was sought. Nonetheless, data was collected following the principles of informed consent, voluntary participation, confidentiality, and anonymity.
The UNICEF Chief of WASH led the data collection process in each country, sometimes in collaboration with government or WHO representatives, or both. The process of data compilation varied from country to country. In most countries, the data collection instrument was populated jointly by staff members of UNICEF and the Ministry of Health, and, where possible, average costs were derived using data from project implementation records or more robust financial data systems. Elsewhere, a stakeholder committee was formed to gather the data, incorporating civil society organizations and other partners into the process. Finally, in some countries UNICEF staff members filled in the instrument based on their implementation experiences. Per their comments, respondents drew on diverse data sources to populate the instrument, including but not limited to national WASH plans and cost norms, project implementation databases, government and partner budgets, and consultations with key informants. A few respondents provided budgeted amounts for recurrent costs, noting a lack of basis to estimate normative costs based on basic service definitions. Individuals who participated in data collection and consented to being named are acknowledged in the main paper. A small number of government officials only agreed to share data on the conditions that country-specific cost estimates would not be published and that their involvement would remain anonymous. No individual is acknowledged in the main paper without their expressed written consent.
Two of the study authors (MC and SM) reviewed all submitted data and sought clarification and additional detail, as needed, from respondents through email communication facilitated by UNICEF. This communication also helped to explain apparent outliers, such as values driven by high construction costs in especially remote or geologically challenging settings. In some cases, adjustments were made to submitted data to increase comparability across countries. For example, multiple respondents excluded from their quantitative submissions drilling costs for onpremises water sources but noted in their qualitative comments the additional costs associated with drilling. In these cases, the drilling costs were incorporated into the per-facility capital costs for on-premises water sources.
There are several possible drivers of cross-country cost variation (see table 2 in the main paper), including differences in local material and labour costs, availability of domestically produced versus imported infrastructure such as pumps and autoclaves, cost premiums to account for implementation in conflict-affected and remote areas, and locally adapted or defined service standards and practices. Additionally, some of the largest variation with single cost categories likely reflects differences in terrain; for instance, all else equal, drilling costs should be lower in the riverbeds of the Greater Mekong Region than in the basalt rock found throughout the Horn of Africa.
Respondents were directed to account for within-country variability when providing average costs, but there is no guarantee that they fully did so. Moreover, differentiating between hospitals and non-hospitals is too coarse to capture the diversity of facility types and configurations in any country, and the different WASH-and waste management-related investments these may require. Likewise, the urban-rural distinction is too crude in countries with large and varied peri-urban settings where costs may be meaningfully different from more purely urban or rural areas. Countries also differ in their technology mix, and many more technologies have the potential to meet basic service standards than those explicitly included in the cost survey. 6 It was not always clear how respondents dealt with variation within specific cost categories. It was assumed that the cost data provided represented the value of investments needed for the average, across all conditions, within a category. However, some respondents provided additional information in the survey comments, or in follow-up correspondence, which reflected an exclusion of outliers from the averages. In these cases, the average costs were adjusted accordingly. d

Missing data
UNICEF did not manage to collect cost information for six LDCs, and several others' submissions were incomplete. A previous global WASH costing exercise favoured an imputation method based on adjusting prices from an economically similar country for differences in per-capita gross domestic product expressed at purchasing power parity. 7 However, exploratory analysis of the cost data from UNICEF's survey failed to reveal any systematic d For example, one response noted that consultations had been conducted with seven regions, and it was estimated that average costs would be 40% higher for the roughly 30% of facilities in challenging environments, such as remote or flood-prone areas. The authors computed a weighted average cost based on these details.
relationship between per-facility costs and potential correlates of cross-country price variation, such as per capita national income or baseline coverage levels. More elaborate econometric methods were also ruled out both due to practical constraints and the prospect that predictive models for 34 different cost variables (17 capital and 17 recurrent) based on data from fewer than 40 countries would be undermined by uncertainty and bias.
Consequently, median per-facility costs were applied when per-facility capital cost estimates were missing. Median values were favoured over arithmetic means because there were notable outliers for most indicators, often attributable to country-specific considerations, explained by survey respondents in their comments or subsequent email communications. The large number of LDCs in Africa allowed for the application of regional medians for missing values in countries located in UNICEF's two operational regions in sub-Saharan Africa (Eastern and Southern Africa; West and Central Africa). For example, no per-facility cost data were received for Niger, so for each capital cost indicator needed, the median value of per-facility capital costs among other LDCs in West and Central Africa was applied. All-LDC medians were applied to countries in other regions (see table S1). e In general, fewer countries reported recurrent costs than capital costs, so the imputation of missing recurrent values used the ratio of recurrent to capital costs where both were reported. Thus, the regional or all-LDC median ratio of recurrent to capital costs was applied to impute missing recurrent costs.
Finally, cost values were extrapolated for on-premises water sources at hospitals. These were not solicited by the per-facility cost survey, but a significant number of LDC hospitals are not connected to piped water sources. f One of the survey respondents included in their comments that the costs associated with on-premises water sources were roughly 40% greater in hospitals than non-hospitals in their country. Based on this, for each country, the capital and recurrent costs for on-premises water sources for hospitals was estimated to be 1.4 times that country's costs for non-hospitals.
e For each cost indicator, the median value was determined only from countries with data for that indicator. The number of countries for which the per-facility cost survey collected data is reported for each capital and recurrent cost indicator in Table 2 of the main paper. f In 17 of the 27 LDCs for which data were available, at least one quarter of hospitals had a non-piped water source.

Section 4: Number of facilities
Extensive internet searches were conducted using Google and Bing to identify the most recently published countryspecific information regarding facility quantities and types. The objective was to find an official facility census or other government documentation for each country, or to find other publications (primarily grey literature) containing this information. Numerous search terms and combinations were employed, including country names and phrases such as "health system overview," "health facilities," "health facility census," and similar. Searches were conducted in English, French, and Portuguese. Up to five pages of search results were reviewed until a suitable source was identified. When this initial search strategy did not yield adequate results, manual searches were conducted of the websites of health ministries, development partners such as WHO, the World Bank, USAID and its implementing partners, and WHO's Health Resources Availability Monitoring System (HeRAMS) (herams.org). When multiple sources were identified for the same country, preference was given to whichever contained more recent data, provided its accounting for facilities appeared to be at least as complete as in other sources. When no recent data could be found, outreach was conducted to individuals familiar with the country's health system.
Providers described as "parapublic" or "mixed" were considered public. When clearly identified, most private facilities were removed from the counts and separately tabulated. These included for-profit facilities and those owned and operated by nongovernmental, not-for-profit, and faith-based organizations. However, some privately owned facilities were retained in the counts in two countries due to their considerable inclusion in government planning or financing (or both): facilities owned by the Christian Health Association of Malawi (CHAM) in Malawi and not-for-profit-owned Centres de Santé Communitaires in Mali. When there was no mention of the private sector, it was assumed that the number of health facilities listed in government and development agency documents pertained only to the public sector. Similarly, when some facilities were stratified by private and public ownership by a source, those facilities not designated as either were assumed to be public. If a source quantified the share of all facilities that were privately owned, the published percentage was applied uniformly to all facility types.
The public health facilities identified were filtered to exclude several types, including those acting as retailers (e.g., pharmacies), or those exclusively providing non-patient-facing services (e.g., laboratories) or auxiliary or allied health services (e.g., dental clinics, school clinics). Mobile clinics and other non-fixed/temporary facilities were also excluded. All other facilities described or implied to be part of the public health system were retained, including military establishments and recognized practitioners of traditional medicine, where relevant. All existing facilities were included regardless of operational status. g No projections were made of expected changes to facility counts between the time of the study and 2030.
The resulting public health facilities were then sorted into four profiles: urban hospitals, urban non-hospitals, rural hospitals, and rural non-hospitals (tables S4 and S5). A portion of published sources provided sufficiently detailed information to assist sorting, such as tables disaggregating facility types across settings, "urban" or "rural" being included in names of facility types, or qualitative information about the concentration of certain facility types in urban or rural areas. For some countries, additional insight was gleaned from email communication with respondents to the cost survey. When sources lacked these details, assumptions guided the sorting as follows: • Where there were multiple hospital types, a determination was made about each type. Those that were considered likely to be referral facilities serving geographies larger than an individual district (e.g., regional, national, or specialized hospitals) were all assumed to be in urban areas; • Other types (e.g., district, municipal, or primary hospitals) were distributed between urban and rural settings in proportion to the country's existing level of urbanization, such that the share of these facilities categorized as urban was equal to the share of the population living in urban areas, 3 with the remainder categorized as rural. This approach is similar to that applied for other global price tags for health (Hanssen O, personal communication); • Where there was only one hospital type, and the country source made no indication that they were all central or referral facilities, they were sorted based on urbanization levels; and • Non-hospitals were also sorted based on urbanization levels.  * Sources that provided private facility data may be incomplete for the private sector. Numbers noted here aggregate all facility types, both hospitals and non-hospitals, except those incorporated in the public facility counts as described in the text above. Otherwise, private sector facilities were not included in the analysis. † Nepal data was sorted with information directly from country representatives rather than by the assumptions in Table S2. ‡ Facility data for Solomon Islands were sourced informally from the Ministry of Health and Medical Services (Tevera A, UNICEF Solomon Islands, personal communication).

Section 5: Quantifying needs
WASH needs were based on 2019 coverage data published by the JMP, which has published estimates of country, regional and global progress toward WASH objectives since 1990. The methods underpinning the JMP's estimates for health care facilities are documented elsewhere. 5,6 The JMP defines service ladders with four rungs for each WASH service: Advanced, Basic, Limited, and No service. Advanced service levels are defined at the country level, not globally, and thus were not included in this multi-country analysis. In this study, the coverage target was that all facilities would meet at least the Basic service level by 2030. Estimating costs for the portion of facilities in the No service category was straightforward: they were assigned the full per-facility costs for the relevant service(s). In contrast, estimating costs for facilities with Limited services would need investments smaller than the full, per-facility costs for the relevant service. However, to best reflect the variation within this category, the estimate of additional costs required additional disaggregation, due to the diversity of on-the-ground realities of facilities at that level, and because the definition of Limited differed by service.

Limited service level for water and sanitation
For water, a facility can fall short of the Basic service level for as little as a broken pipe and as much as the lack of an on-premises water source. Similarly, for sanitation, a facility might not meet the basic service levels due to the lack of waste bins for menstrual hygiene products or there only being one improved toilet but no sex separation or accommodation for those with limited mobility. Consequently, data were sought that would allow division of the Limited category between facilities requiring investment of comparable magnitude to those at the No service level (full investment) and those whose needs were somewhat less (reduced investment). Other JMP indicators allowed for identification of the portion of facilities with or without the core water and sanitation infrastructure that was assumed to drive the largest portion of capital costs: an improved, on-premises water source and an improved, usable sanitation facility. Facilities with those assets were then assumed only to require reduced capital investment, while facilities lacking those assets were assumed to require the same capital investment as facilities at the No service level.
In the absence of more granular cost or needs data, consultations were conducted with an expert steering group to determine what share of the per-facility capital costs should be applied to the facilities requiring reduced investment for water or sanitation (table S6). The steering group determined that the main estimates should assume that reduced investments would require 50% of the per-facility capital costs. In other words, it was assumed that, on average, sub-standard facilities that already had the core water or sanitation infrastructure would require half as much upfront investment as facilities at or above the Basic service level. Recognizing considerable uncertainty in this assumption, the steering group recommended producing two additional cost estimates, lower and upper, using 15% and 85% of the per-facility capital costs, respectively, for the facilities that required reduced investments. Additionally, the steering group agreed that all in-need facilities-those requiring either full or reduced investment-required 100% of the per-facility recurrent costs for water and sanitation. Therefore, these were not varied in the lower and upper estimates.

Limited service level for hygiene and waste management
For hygiene, a facility is at the Limited service level if hand hygiene facilities are available at points of care or within five metres of sanitation facilities, but not at both. There are JMP indicators for both locations within facilities, allowing separate calculations of how many facilities lack hygiene at points of care and in proximity to toilets. These two indicators together capture all facilities at the Limited and No service levels. Similarly, the Limited service level for waste management involves some level of waste segregation, treatment, and disposal, and the JMP has separate indicators for (i) segregation and (ii) treatment and disposal.
In the absence of more granular cost or needs data, assumptions were made, again informed by consultations with the steering group and additional experts, regarding the portion of per-facility costs applied to the reduced investment categories (table S7). For hygiene, non-hospitals were assumed to have an equal number of points of care and sanitation facilities, so a "reduced investment" in this category meant that 50% of the per-facility costs for sanitation facilities and points of care were used. Hospitals were assumed to have three times as many points of care as sanitation facilities, so "reduced investments" for this type of facility were assumed to be equivalent to 75% and 25% of the per-facility costs for points of care and toilets, respectively. For waste management, treatment and disposal was assumed to be much more capital intensive than segregation (incinerators and autoclaves cost more than waste bins), while the reverse was assumed for recurrent needs. Thus, for facilities lacking waste segregation, the "reduced investment" was estimated as being equal to 25% of the per-facility capital costs and 75% of the perfacility recurrent costs for waste management. Similarly, for facilities lacking waste treatment and disposal, the "reduced investment" was estimated as being equal to 75% of the capital and 25% of the recurrent costs of the full per-facility costs for waste management (Pieper U, personal communication).

Matching JMP indicators to facility profiles
The JMP publishes estimates of national service coverage levels for WASH in health care facilities, broken down by several stratifications: urban and rural facilities, hospital and non-hospital facilities, and government and nongovernment facilities. The last stratification was not used in this analysis given the exclusion of private facilities in the estimation of country-level resource needs. These, however, are not available for combinations of these stratifications, and so sorting rules were defined to match coverage indicators to the four facility profiles used in the cost analysis. Preference was given to either of the JMP strata matching the facility type (e.g., for rural nonhospitals, the JMP estimate for rural facilities was applied if available; if not, the estimate for non-hospitals was applied).
When the JMP lacked estimates for either of the two preferred strata for a particular facility profile, other values for the same country were used as proxies. Candidate strata were ranked based on correlation analysis using countries with data for multiple strata (table S8). For any given stratum, the Kendall Tau correlation coefficient was computed utilizing each other stratum to determine the preference order. Any stratum yielding a coefficient of at least 0.5 was considered to be a valid proxy and ordered from greatest to least correlation coefficient. i When repeated with Spearman's Rho and Pearson's correlation tests, the results were broadly the same. All correlation analysis was conducted in R using the "cor" function and restricting analysis to complete observations.
If no data were available and all candidate proxies failed to pass the correlation test, the LDC average j from the corresponding first choice stratum was applied. k Some manual adjustments were required when the application of LDC averages led to negative results when one coverage indicator was subtracted from another. l DNQ = did not qualify based on the 0.5 correlation coefficient threshold; JMP = Joint Monitoring Programme Note: Correlation analysis was conducted on a single JMP indicator type for each service: improved and onpremises source for water (_imop), improved and usable for sanitation (_ius), points of care for hygiene (_poc), and segregation for waste management (_seg).
i Kendall Tau was selected due to the non-parametric nature of the coverage data (bounded by 0% and 100%) and its tendency to yield smaller coefficients than Spearman Rho, the other common correlation coefficient for non-parametric data. The rationale was to favour a more conservative test (i.e., the one from which estimated correlations would be less likely to clear the threshold for proxy validity). Below the threshold of 0.5, it was assumed that a regional or all-LDC average for the same facility type would be superior to a same-country value for a different facility type. j The LDC averages published by the JMP at the time of data collection were based on the 47 countries classified as LDCs prior to Vanuatu's graduation in December 2020. Vanuatu represented only 0·03% of the total population across all LDCs and so did not have any measurable impact on LDC averages (Johnston R, personal communication). k The JMP did not have estimated LDC averages for all strata. For sanitation, the non-hospital value was used when the model sought the urban value, and the national value was used when the model sought the hospital value. Similarly, for hygiene the non-hospital value was used when the model sought the national, urban, or hospital value. l For five countries, application of the LDC average to water coverage led to a negative result, and thus, a value was assigned to wat_bas (basic) that was equal to 50% of the value of wat_imop (improved and on premises). Similarly, for sanitation coverage, application of the LDC average led to a negative result for one country. In this case, a value was assigned to san_ius (improved and usable) that was equivalent to san_bas (basic).

Exclusion of environmental cleaning
There are far fewer data for coverage of environmental cleaning than other WASH services. The JMP only had national coverage estimates for four LDCs (Bhutan, Malawi, Niger, and Rwanda) and some stratified estimates for an additional four (Ethiopia, Mali, Mozambique, and Zambia). These countries' combined population was too small for the JMP to impute an all-LDC average. m Along with the mismatch between the definition of basic cleaning services and the UNICEF cost data for cleaning, this contributed to the decision to exclude environmental cleaning from the analysis.
m The JMP produces estimates for country groupings when data are available for countries containing at least 30% of the grouping's total population.

Section 6: Water and sanitation service assumptions
Although a range of technologies can be deployed to meet the basic service standards for WASH in health care facilities, 6 for water and sanitation, the per-facility cost survey narrowed the options to only two categories each. Many more are used in practice. The resulting database contained separate cost estimates for piped and on-premises water sources and for sewerage-and septic-based sanitation systems. Consequently, assumptions were made about what share of in-need facilities would require costs associated with networked versus other systems for water and sanitation services.
It was assumed that in-need facilities would incur the costs of networked services-piped water and sewerage-linked sanitation-in proportion to the baseline availability of those systems in a country. By extension, all other facilities would be assumed to require the costs for non-networked services (on-premises water sources and septic-based sanitation). The model did not attempt to anticipate increases in the availability of networked infrastructure in the decade ending in 2030, nor did it estimate the more general infrastructure costs that such increases would entail.
Information about the prevalence of networked water and sanitation services came from three sources: 1. In some cases, respondents to the per-facility cost survey included information about the universality or unavailability of certain kinds of services in their country. This information was accepted except under specific circumstances (see next item). 2. Relevant data were extracted from JMP country files, which consolidate information about health infrastructure from a range of nationally representative surveys. For each JMP stratum (national, urban, rural, hospital, and non-hospital), the most recent estimate was sought for the prevalence of piped water and sewerage-linked sanitation in health care facilities. Data were only accepted if the source survey was published after 2010 and considered sufficiently representative to be used in the JMP's own analysis. These data overrode information gleaned from cost survey comments in rare instances of significant disagreement. n In total, the estimated prevalence of networked services was found for 34 countries for water and 16 countries for sanitation. 3. When it was not possible to determine the prevalence of service types from either of the first two sources, the shares of households with piped water or sewerage-linked sanitation from the JMP household data o for 2017 were used as proxies. p The data on service types were stratified identically to the coverage data, so the same correlation analysis technique was applied to match those strata to the facility profiles in the model (table S9; section 5). The household WASH service data were only stratified between urban and rural settings.
n If a UNICEF survey respondent indicated that a technology was not available in the country, but data from the WHO/UNICEF JMP country file suggested a prevalence of 10% of greater, the latter prevailed. This occurred for only three of the 92 technology prevalence estimates: sanitation in Bangladesh, sanitation in Haiti, and water in the Solomon Islands. o WHO/UNICEF JMP country files and household data are available at washdata.org. p For Central African Republic and Eritrea, the most recent household data were from 2016 and were applied.

Section 7: Modelling scale-up and asset replacement
The estimates presented are based on a model that assumes a linear, ten-year scale-up, from baseline coverage levels of WASH and waste services in health care facilities to full coverage of the basic service level by 2030. Initial capital investments were assumed to be equally distributed across the ten years, such that 10% of the capital costsnot including replacement costs-were distributed to each year. Due to the urgent need of these investments given their potential contributions to the achievement of multiple SDGs, capital investments would ideally be front-loaded. In fact, the global targets for WASH in health care facilities include 80% coverage by 2025. However, LDCs are among the countries with the greatest resource constraints and least absorptive capacity, 53 raising doubts about the ability of LDC governments and their partners to rapidly finance and build large quantities of assets. Consequently, linear scale-up may be the most ambitious investment trajectory that would be feasible in LDCs.
Each year, additional recurrent costs were estimated in proportion to that year's accumulated capital investments, such that annual recurrent costs were relatively small in 2021 and increased annually through 2030. Finally, because some newly installed assets were not expected to last through 2030 (tables S10 and S11), replacement costs were estimated separately to reflect the need for countries to make additional rounds of capital investments as assets expired. The total capital costs presented in the findings included both the initial capital costs and all replacement costs.
The timing of replacement costs was based on the average expected lifespan of WASH infrastructure. Published studies and technology specifications were reviewed to determine indicative lifespans for WASH technologies deployed in households, communities, health care facilities, and other institutional settings.
All the water and sanitation technologies used in the baseline estimates were assumed to have lifespans of at least ten years, so they did not to require replacement during the period from 2021 through 2030. Hygiene technologies in facilities with piped water were also assumed to last at least ten years, given the estimated lifespan of a sink with tap. In all other facilities, new hygiene capital was replaced after two years in the model. For waste management, hospitals were assumed to use sterilization and incineration technologies that were expected to last at least ten years. Non-hospitals were assumed to use De Montfort incinerators or similar technologies, which were expected to be replaced after four years in the model. In the upper estimates, the average expected lifespans of certain water and sanitation assets were shortened due to expected impacts of more frequently occurring extreme weather events, a consequence of climate change (see section 8).
The full value of replacement costs was fed into the model following the year of asset expiration. For example, some facilities lacking piped water received new hygiene assets in year 1 with an expected lifespan of two years, triggering replacement costs in years 3, 5, 7, and 9. Similarly, some non-hospitals received new waste management assets in year 1 with an expected lifespan of four years, triggering replacement costs in years 5 and 9.

Section 8: Sensitivity analysis
Due to uncertainty in several model assumptions, lower and upper estimates were produced by varying selected parameters (table S12). First, alternative assumptions were imposed to address variation within the Limited coverage category for water and sanitation services. As detailed in section 5, in the baseline estimates, facilities with major water or sanitation assets-i.e., those with an on-premises and improved water source or improved and usable sanitation facility-but failing to meet the Basic service level were assumed to require reduced investment amounting to half as much capital investment as facilities lacking any water or sanitation services. Based on consultation with the expert steering group (see section 1), it was alternatively assumed that these facilities required only 15% of the full per-facility capital cost in the lower estimates, as if they only needed minor upgrades. Similarly, it was assumed these facilities required 85% of the full per-facility capital cost in the upper estimates, as if they required major upgrades or rehabilitation approaching the cost of replacement.
Second, there is inherent uncertainty to any discount rate applied to future costs. The baseline estimates were based on an assumed annual discount rate of 5%. Arguments were considered for other discount rates. For example, methods promoted by both the Global Health Costing Consortium 68 and WHO's Choosing Interventions that are Cost-Effective (CHOICE) 69 include the use of a 3% annual discount rate for costs. In contrast, historical World Bank guidance on the economic evaluation of investments in low-and middle-income countries favoured discount rates greater than 10% due to high costs of capital in those settings. 70 Discount rates in part represent the opportunity cost of present versus future consumption, a function of expected economic growth. 71 Future growth is unpredictable, particularly in the wake of a major shock, such as that caused by the ongoing global SARS-CoV-2 pandemic. The economic recovery may occur with greater, equal, or lesser speed than expected at global and national levels. Consequently, the lower and upper estimates incorporated alternative discount rates: 8% for the lower estimates, reflecting the possibility of unexpectedly rapid economic growth between 2021 and 2030, and 3% for the upper estimates, reflecting the possibility of slower-than-expected growth during that period. This approach has the additional benefit of aligning with the discount rates applied in the baseline, lower, and upper estimates in previous costing of global WASH targets for households, as defined under SDG 6. 7 Finally, climate change is increasing the frequency and severity of climate hazards, such as floods and droughts, that can degrade WASH infrastructure. 72 The model incorporated replacement costs into estimated capital costs for those assets with average expected lifespans of less than ten years. For the baseline estimates, these included hygiene assets in health care facilities with non-piped water and waste management assets in non-hospitals (see section 7). Because the relevant climate risks are one-tailed-future climatological conditions could shorten but not lengthen expected asset lifespans-they were only factored into the upper estimates. To reflect the greater likelihood that water and sanitation assets will be harmed by climate hazards, the expected lifespans of on-premises water sources and septic-based sanitation systems were shortened in the upper estimates from greater than ten years to seven years. Networked water and sanitation assets were assumed to be more climate resilient 72 and thus retained expected lifespans of greater than ten years.  Figure S1. Percentage effect of varying key parameters on capital and recurrent costs compared to the lower and upper estimates (one-way sensitivity analysis)

Section 9: Benchmark analysis
Information is scant regarding past and current spending levels in LDCs on WASH and waste services in health care facilities. Consequently, a funding gap analysis, which would rely on trend-based forecasts of expected spending on WASH and waste services in LCDs, was not feasible for this study. Related, no attempt was made to estimate the effectiveness or efficiency of current spending.
Instead, a basic benchmarking analysis against existing spending levels was carried out to give an indicative sense of financial feasibility for the scale-up of basic service levels in the LDCs existing public health facilities.
Secondary data were compiled from multiple sources for four relevant comparator categories of expenditure (table  S14). First, data on capital health expenditure per capita, financed by both domestic public and external sources, was retrieved from WHO's Global Health Expenditure Database (GHED) 73 , with estimates available for 23 LDCs. The most recent estimates available from 2015 onward were taken, which were expressed in 2018 US$.
Second, current health expenditure per capita financed by domestic public sources (GGHE-D per capita) in 2018 was also retrieved from the GHED, with estimates available for all but two LDCs. The GHED does not contain data from Somalia, and no 2018 value was reported for Yemen.
Third, WASH expenditure per capita data were reported in the UN-Water global analysis and assessment of sanitation and drinking-water (GLAAS) 2019 report for 22 LDCs. As part of GLAAS, countries self-reported spending from one of their budget years between 2017 and 2019. 74 Finally, data on aid disbursements in 2019 for WASH in LDCs were retrieved from the OECD's Creditor Reporting System (CRS). 75 Data were downloaded from the CRS for Water Supply and Sanitation (sector 140) including all official donors, all channels, all aid types, and both Official Development Assistance (ODA) and Other Official Flows.
For all four benchmarks, population-adjusted estimates for per-capita expenditure across the LDCs were computed, in each case reflecting only the countries for which expenditure estimates were available. National population data were drawn from the World Population Prospects 76 for the years corresponding to the expenditure data. For example, the main paper alludes to the US$0·80 invested in health capital by 23 LDC governments in 2018-this amount was calculated through the following steps (an analogous process was followed for the other three spending benchmarks): 1. Retrieving from the GHED the estimates for those 23 countries' government expenditure per capita on health capital in 2018; 2. Multiplying each by their respective country's population in 2018 to compute total government expenditure on health capital; 3. Dividing that amount by the sum of the countries' populations.